Accurate description of the constitutive relation and the material parameters are necessary for constructing accurate digital twins of structures. Model updating-based parameter identification is a popular approach employing numerical models such as FEM to build and validate the accuracy of the digital twins based on real-world measurements. However, the real-world structure’s material parameters and constitutive relation might differ from the ideal properties used in the numerical model. Additionally, the material parameters and behaviour change due to ageing of the structure, which is critical to predict the remaining life of the structure. Common SHM approaches focus on identifying the material parameters such as Young’s modulus of the structure to detect an anomaly [1,2]. While this information represents the current state of the structure accurately, prediction regarding the remaining life of the structure is challenging as the underlying constitutive behaviour has likely changed. This highlights the necessity of identifying material model (parameters and constitutive) altogether for an accurate predictive digital twin.In this work, we address the material model identification problem by first decoupling it into two manageable sub-problems, material parameters identification and constitutive model identification, to gain a deeper understanding of each component’s characteristics. The parameter identification includes identifying the material constants such as the Young’s modulus, Poisson’s ratio, thermal expansion coefficient, etc, given a constitutive law. On the other hand, the constitutive model identification discovers the constitutive model (such linear elastic, non-linear, hyper-elastic, etc) influencing the material behaviour given a set of material parameters. Both the sub-problems are formulated as adjoint-based sensitivities driven optimization tasks with the objective to minimize the weighted differences between the model and the deformation measurements obtained from displacement or strain sensors.The parameters identification follows the continuous optimization technique proposed by the authors in [1,2]. For the constitutive model identification, the continuous relaxation approach is used to convert the discrete (list of constitutive models) variable optimization to acontinuous problem enabling smoother exploration. Different levels of fidelities for both thesub-identification problems are investigated, ranging from low-fidelity global parameters to high-fidelity element-based parameters. To tackle the ill-conditioning of high-fidelity problems, filtering and regularization becomes critical, and techniques such as Vertex Morphing are explored to stabilize the solution. The material parameters and constitutive model identification problems are subsequently combined and the comprehensive material model identification problem is formulated. The results and challenges arising from this combined optimization are examined in detail. Various structural problems and different material models are analysed to demonstrate the effectiveness of the proposed approach.REFERENCES[1] Facundo N Airaudo, Rainald Löhner, Roland Wüchner, and Harbir Antil. “Adjoint-based determination of weaknesses in structures”. In: Computer Methods in Applied Mechanics and Engineering 417 (2023), p. 116471.[2] Rainald Löhner, Facundo Airaudo, Harbir Antil, Roland Wüchner, Fabian Meister, and Suneth Warnakulasuriya. “High-Fidelity Digital Twins: Detecting and Localizing Weaknesses in Structures”. In: AIAA SCITECH 2024 Forum. 2024, p. 2621.
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Accurate description of the constitutive relation and the material parameters are necessary for constructing accurate digital twins of structures. Model updating-based parameter identification is a popular approach employing numerical models such as FEM to build and validate the accuracy of the digital twins based on real-world measurements. However, the real-world structure’s material parameters and constitutive relation might differ from the ideal properties used in the numerical model. Additi...
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